Haiko Schurz

Three commercial artificial intelligence (AI)–based computer-aided detection systems showed elevated AI scores for screening mammograms up to 10 years before breast cancer diagnosis, demonstrating their potential use in flagging individuals for supplemental screening and earlier detection.

Previously, the International Tuberculosis Host Genetics Consortium (ITHGC) demonstrated the power of large-scale GWAS analysis across diverse ancestries in identifying tuberculosis (TB) susceptibility loci (Schurz et al., 2024). Despite identifying a significant genetic correlate in the human leukocyte antigen (HLA)-II region, this association did not replicate in the African ancestry-specific analysis, due to small sample size and the inclusion of admixed samples. Our study aimed to build upon the findings from the ITHGC and identify TB susceptibility loci in an admixed South African cohort using the local ancestry allelic adjusted association (LAAA) model. We identified a suggestive association peak (rs3117230, p-value = 5.292 × 10^-6, OR = 0.437, SE = 0.182) in the HLA-DPB1 gene originating from KhoeSan ancestry. These findings extend the work of the ITHGC, underscore the need for innovative strategies in studying complex admixed populations, and confirm the role of the HLA-II region in TB susceptibility in admixed South African samples.

The International Tuberculosis Host Genetics Consortium (ITHGC) demonstrated the power of large-scale GWAS analysis across diverse ancestries in identifying tuberculosis (TB) susceptibility loci. Despite identifying a significant genetic correlate in the human leukocyte antigen (HLA)-II region, this association did not replicate in the African ancestry-specific analysis, due to small sample size and the inclusion of admixed samples. Our study aimed to build upon the findings from the ITHGC and identify TB susceptibility loci in an admixed South African cohort using the local ancestry allelic adjusted association (LAAA) model. We identified a suggestive association peak (rs3117230, p-value = 5.292 ×10−6, OR = 0.437, SE = 0.182) in the HLA-DPB1 gene originating from KhoeSan ancestry. These findings extend the work of the ITHGC, underscore the need for innovative strategies in studying complex admixed populations, and confirm the role of the HLA-II region in TB susceptibility in admixed South African samples.

Polygenic risk scores (PRS) have emerged as a promising tool for predicting disease risk and treatment outcomes using genomic data. Thousands of genome-wide association studies (GWAS), primarily involving populations of European ancestry, have supported the development of PRS models. However, these models have not been adequately evaluated in non-European populations, raising concerns about their clinical validity and predictive power across diverse groups. Addressing this issue requires developing novel risk prediction frameworks that leverage genetic characteristics across diverse populations, considering host-microbiome interactions and a broad range of health measures. One of the key aspects in evaluating PRS is understanding the strengths and limitations of various methods for constructing them. In this review, we analyze strengths and limitations of different methods for constructing PRS, including traditional weighted approaches and new methods such as Bayesian and Frequentist penalized regression approaches. Finally, we summarize recent advances in PRS calculation methods development, and highlight key areas for future research, including development of models robust across diverse populations by underlining the complex interplay between genetic variants across diverse ancestral backgrounds in disease risk as well as treatment response prediction. PRS hold great promise for improving disease risk prediction and personalized medicine; therefore, their implementation must be guided by careful consideration of their limitations, biases, and ethical implications to ensure that they are used in a fair, equitable, and responsible manner.

Data harmonization involves combining data from multiple independent sources and processing the data to produce one uniform dataset. Merging separate genotypes or whole‐genome sequencing datasets has been proposed as a strategy to increase the statistical power of association tests by increasing the effective sample size. However, data harmonization is not a widely adopted strategy due to the difficulties with merging data (including confounding produced by batch effects and population stratification). Detailed data harmonization protocols are scarce and are often conflicting. Moreover, data harmonization protocols that accommodate samples of admixed ancestry are practically non‐existent. Existing data harmonization procedures must be modified to ensure the heterogeneous ancestry of admixed individuals is incorporated into additional downstream analyses without confounding results. Here, we propose a set of guidelines for merging multi‐platform genetic data from admixed samples that can be adopted by any investigator with elementary bioinformatics experience. We have applied these guidelines to aggregate 1544 tuberculosis (TB) case‐control samples from six separate in‐house datasets and conducted a genome‐wide association study (GWAS) of TB susceptibility. The GWAS performed on the merged dataset had improved power over analyzing the datasets individually and produced summary statistics free from bias introduced by batch effects and population stratification. © 2024 The Author(s). Current Protocols published by Wiley Periodicals LLC.Basic Protocol 1: Processing separate datasets comprising array genotype dataAlternate Protocol 1: Processing separate datasets comprising array genotype and whole‐genome sequencing dataAlternate Protocol 2: Performing imputation using a local reference panelBasic Protocol 2: Merging separate datasetsBasic Protocol 3: Ancestry inference using ADMIXTURE and RFMixBasic Protocol 4: Batch effect correction using pseudo‐case‐control comparisons

Globally, tuberculosis (TB) presents with a clear male bias that cannot be completely accounted for by environment, behaviour, socioeconomic factors, or the impact of sex hormones on the immune system. This suggests that genetic and biological differences, which may be mediated by the X chromosome, further influence the observed male sex bias. The X chromosome is heavily implicated in immune function and yet has largely been ignored in previous association studies. Here we report the first multi-ancestry X chromosome specific meta-analysis on TB susceptibility. We identified X- linked TB susceptibility variants using seven genotyping data sets and 20,255 individuals from diverse genetic ancestries. Sex-specific effects were also identified in polygenic heritability between males and females along with enhanced concordance in direction of genetic effects for males but not females. These sex-specific genetic effects were supported by a sex-stratified and combined meta- analysis conducted using the X chromosome specific XWAS software and a multi-ancestry analysis using the MR-MEGA software. Seven significant associations were identified. Two in the overall analysis (rs6610096, rs7888114) and a second for the female specific analysis (rs4465088) including all data sets. For the ancestry specific meta-analysis three significant associations were identified for males in the Asian cohorts (rs1726176, rs5939510, rs1726203) and one in females for the African cohort (rs2428212). Several genomic regions previously associated with TB susceptibility were reproduced in this study, along with strong ancestry-specific effects. These results support the hypothesis that the X chromosome and sex-specific effects could significantly impact the observed male bias in TB incidence rates globally.

The International Tuberculosis Host Genetics Consortium (ITHGC) demonstrated the power of large-scale GWAS analysis across diverse ancestries in identifying tuberculosis (TB) susceptibility loci. Despite identifying a significant genetic correlate in the human leukocyte antigen (HLA)-II region, this association did not replicate in the African ancestry-specific analysis, due to small sample size and the inclusion of admixed samples. Our study aimed to build upon the findings from the ITHGC and identify TB susceptibility loci in an admixed South African cohort using the local ancestry allelic adjusted association (LAAA) model. We identified a suggestive association peak ( rs3117230 , p -value = 5.292 ×10 ⁻⁶ , OR = 0.437, SE = 0.182) in the HLA-DPB1 gene originating from KhoeSan ancestry. These findings extend the work of the ITHGC, underscore the need for innovative strategies in studying complex admixed populations, and confirm the role of the HLA-II region in TB susceptibility in admixed South African samples.

The colonial-period arrival of Europeans in southern Africa is associated with strong sex-biased migration by which male settlers displaced indigenous Khoekhoe and San men. Simultaneously, the importation of South Asian, Indonesian and Eastern African slaves may have contributed female-biased migration to Cape Town and surrounding areas. We examine the spatial and temporal spread of sex-biased migration from the Cape northward into Namaqualand and the southern Kalahari using genetic data from more than 1,400 individuals. In all regions, admixture patterns were sex-biased, with evidence of a greater male contribution of European ancestry and greater female contribution of Khoe-San ancestry. While admixture among Khoe-San, European, equatorial African, and Asian groups has likely been continuous from the founding of Cape Town to present-day, we find that Khoe-San groups further north experienced a single pulse of European admixture 6-8 generations ago. European admixture was followed by additional Khoe-San gene flow, potentially reflecting an aggregation of indigenous groups due to disruption by colonial interlopers. Male migration into the northern frontier territories was not a homogenous group of expanding Afrikaners and slaves. The Nama show evidence of distinct founder effects and derive 15% of their male lineages from Asian men, a pattern absent in the ≠Khomani San. Khoe-San ancestry from the paternal line is greatly diminished in populations from Cape Town, the Cederberg Mountains and Upington, but remains more frequent in self-identified ethnically indigenous groups. Strikingly, we estimate that Khoe-San Y-chromosomes were experiencing unprecedented population growth at the time of European arrival. Our findings shed light on the patterns of admixture and the population history of South Africa as the colonial frontier expanded.

Summary: IL-6 responses are ubiquitous in Mycobacterium tuberculosis (Mtb) infections, but their role in determining human tuberculosis (TB) disease risk is unknown. We used single nucleotide polymorphisms (SNPs) in and near the IL-6 receptor (IL6R) gene, focusing on the non-synonymous variant, rs2228145, associated with reduced classical IL-6 signalling, to assess the effect of altered IL-6 activity on TB disease risk. We identified 16 genome wide association studies (GWAS) of TB disease collating 17,982 cases of TB disease and 972,389 controls across 4 continents. Meta-analyses and Mendelian randomisation analyses revealed that reduced classical IL-6 signalling was associated with lower odds of TB disease, a finding replicated using multiple, independent SNP instruments and 2 separate exposure variables. Our findings establish a causal relationship between IL-6 signalling and the outcome of Mtb infection, suggesting IL-6 antagonists do not increase the risk of TB disease and should be investigated as adjuncts in treatment.

The heritability of susceptibility to tuberculosis disease (TB) has been well recognized. Over one-hundred genes have been studied as candidates for TB susceptibility, and several variants were identified by genome-wide association studies (GWAS), but few replicate. We established the International Tuberculosis Host Genetics Consortium (ITHGC) to perform a multi-ancestry meta-analysis of GWAS including 14153 cases and 19536 controls of African, Asian, and European ancestry. Our analyses demonstrate a substantial degree of heritability (pooled polygenic h ² =26.3% 95% CI 23.7-29.0%) for susceptibility to TB that is shared across ancestries, highlighting an important host genetic influence on disease. We identified one global host genetic correlate for TB at genome-wide significance (p−8 ) in the human leukocyte antigen (HLA)-II region (rs28383206, p-value = 5.2×10 ⁻⁹ ). These data demonstrate the complex shared genetic architecture of susceptibility to TB and the importance of large scale GWAS analysis across multiple ancestries experiencing different levels of infection pressures.